The Business Case for Product Data Excellence
The average seniority of a data leader in an organization is only 17 months. There are few organizational functions where survivability is so low. The obvious question: why is it so difficult for the world’s data leaders to succeed?
In his keynote address at IRM UK last year, Roberto Maranca, now Vice President of Data Excellence at Schneider Electric and previously Director of Data at Lloyds Banking Group and GE, aptly explained that There are three main reasons data teams fail.
First, data is tribal. Within every organization, there are “tribes” of people who share beliefs, work practices and, most importantly, a dialect. This dialect inevitably colors the data they produce or manipulate. When this data leaves the tribe, it is often perceived as unfit for consumption by people who do not belong to that same tribe.
Second, the data is an afterthought. A lot of projects depend on it in a very fundamental sense, but by the time people realize that the underlying data is not good enough, the budget is almost done, the deadline is approaching and there is no longer the time to go back and fix things. The project is therefore moving forward with unframed data.
Finally (but not least!), The benefits of data are still very intangible. It is difficult to quantify the results produced by the work you do. While everyone agrees that “data is the new oil,” many data teams struggle to produce a solid business case.
The business case for product data
Let’s focus on this last point in the area of product data in particular. How can a PIM executive create a rock-solid business case for their project and ensure buy-in and successful execution?
Conventional wisdom holds that the closer you are to the customer, the easier it is to quantify the benefits. Perhaps more precise would be to say “the closer you are to the transaction”.
The product data is indeed very close to the transaction. 81% of shoppers research products online before making a decision, whether the product is then purchased online or offline. A large majority of purchases, and therefore – most of the organization’s revenue is heavily influenced by the digital content of the product.
This tells us that there is a solid business case for better product data. Now how do we calculate it?
It helps to think of two buckets of benefits.
First, let’s quantify the benefits associated with market reach. Here we can include:
- Faster time to market for products launched by your organization
This is a very tangible and easily quantifiable benefit. All you need to hit the $$$ number is to understand how quickly you can start selling a new product and what that translates to in terms of lost revenue right now.
- Faster adoption of new routes to market
It could be new distribution or retail partners, new countries, new markets, new advertising opportunities. All of these require your product data, each – in their preferred way and format, with specific customization requirements. Again, by determining how quickly you can access a new sales channel, for example, you can calculate how quickly you can start generating income from that channel and what that means in absolute numbers.
What would it mean for your organization if you could support more sales and marketing channels at once (and keep introducing new ones) without creating a bottleneck in your data teams? Also put an amount of $$$ on it.
Second, let’s take a look at the benefits associated with improving the quality of product data. The reasoning is as follows:
- Excellence in PIM enables you to create and maintain up-to-date, rich, and comprehensive content for each product.
- Excellence in product data syndication enables you to distribute this product content to all of your business partners and customers in their preferred format and method.
- As a result, your product listings are well described, up-to-date, and appealing to buyers – and earn a greater share of organizational budgets and consumer portfolios compared to competing products.
Now, quantifying this is a bit trickier than the benefits of market reach.
What you are looking for is an increase in both discoverability and conversions for your products. What counts as a good increase, of course, varies by sales channel and product category.
You want to make industry specific assumptions and then look at the results. Be aware that results will come with a delay, as it takes time for the content of the product you distribute to trickle down the value chain and present itself to buyers looking to make purchasing decisions.
Good assumptions will likely require segmentation by sales channel, as these will create a different context for your products from a buyers’ perspective. Thus, a different approach is needed to get a good fit with the context of each channel.
Hope this gives you a good start in connecting data excellence with tangible value for your organization.